ABSTRACT Systems genetics studies utilize a diverse array of experimental, computational and statistical approaches to interrelate genotypes and phenotypes in order to provide a comprehensive view of the underlying genetic architecture of complex traits. The Systems Genetics and Bioinformatics Core (Core C) serves as the catalyst for integration for the U19 across data types, viruses and species to enable the identification of candidate genes, pathways and networks implicated in human diseases and provides context for the loci identified in genome-wide association studies (GWAS) with regard to their contribution to disease susceptibility. Core C will focus on robust and reproducible approaches to facilitate identification of candidate genes and gene networks involved in innate, adaptive, and memory immunity, as well as prioritization and candidate refinement in close coordination with all projects and Core B (Mouse Genetics). The goal of this Core is to provide focus for empirical investigation and validation of the computational predictions. Specifically, by employing integrative, systems-level approaches dissecting immunological traits, we are able to elucidate key drivers of immune host response beyond what could be achieved by traditional genetic association studies alone. Core C will facilitate hypothesis-driven (candidate gene) and hypothesis-generating (network) analyses to address the U19 goals. In some cases an underlying candidate gene may be the same in animal models and humans and will be prioritized for further study. In other cases, animal model research or our analysis of the human responses and genetic variants that direct these responses may identify a gene network relevant in humans, with a hub that could be manipulated in the mouse model to examine network effects. Analyses of both candidate genes and networks will be more powerful than either alone to provide the interlocking levels of proof to move from gene/network to mechanism. Successful implementation and execution of the proposed research in each project necessitates robust cutting-edge analytical methods and computational workflows. Core C personnel have significant experience in systems genetics (both statistical genetics and systems biology) that includes computational modeling and network inference, in addition to the detection of QTLs in a highly complex genetic background, as well as expertise in management and dissemination of large scale genetic and genomic resources.